docstr: Creates some test data.
  
  Batch size is fixed to 2 for all tests.
docstr_tok: ["Creates","some","test","data",".","Batch","size","is","fixed","to","2","for","all","tests","."]
code: def _get_test_data(self, num_points, device, dtype):
      """Creates some test data.
  
      Batch size is fixed to 2 for all tests.
      """
      batch_size = 2
      torch.manual_seed(84)
  
      tau = 2 * 3.141592653589793
      axis_angle_1 = self._get_samples(
          shape=(1, 3), low=-tau, high=tau, dtype=dtype, device=device
      )
      axis_angle_2 = self._get_samples(
          shape=(1, 3), low=-tau, high=tau, dtype=dtype, device=device
      )
      rotation_1 = kornia.geometry.axis_angle_to_rotation_matrix(
          axis_angle_1
      )
      rotation_2 = kornia.geometry.axis_angle_to_rotation_matrix(
          axis_angle_2
      )
  
      translation_1 = self._get_samples(
          shape=(3,), low=-100, high=100, dtype=dtype, device=device
      )
      translation_2 = self._get_samples(
          shape=(3,), low=-100, high=100, dtype=dtype, device=device
      )
  
      temp = torch.eye(4, dtype=dtype, device=device)
      world_to_cam_mats = temp.unsqueeze(0).repeat(batch_size, 1, 1)
      world_to_cam_mats[0, :3, :3] = torch.squeeze(rotation_1)
      world_to_cam_mats[0, :3, 3] = translation_1
      world_to_cam_mats[1, :3, :3] = torch.squeeze(rotation_2)
      world_to_cam_mats[1, :3, 3] = translation_2
  
      intrinsic_1 = torch.tensor(
          [[500.0, 0.0, 250.0], [0.0, 500.0, 250.0], [0.0, 0.0, 1.0]],
          dtype=dtype,
          device=device,
      )
  
      intrinsic_2 = torch.tensor(
          [[1000.0, 0.0, 550.0], [0.0, 750.0, 200.0], [0.0, 0.0, 1.0]],
          dtype=dtype,
          device=device,
      )
  
      intrinsics = torch.stack([intrinsic_1, intrinsic_2], dim=0)
  
      cam_points_xy = self._get_samples(
          shape=(batch_size, num_points, 2),
          low=-100,
          high=100,
          dtype=dtype,
          device=device,
      )
      cam_points_z = self._get_samples(
          shape=(batch_size, num_points, 1),
          low=0.5,
          high=100,
          dtype=dtype,
          device=device,
      )
      cam_points = torch.cat([cam_points_xy, cam_points_z], dim=-1)
  
      repeated_intrinsics = intrinsics.unsqueeze(1).repeat(
          1, num_points, 1, 1
      )
      world_points, img_points = self._get_world_points_and_img_points(
          cam_points, world_to_cam_mats, repeated_intrinsics
      )
      world_to_cam_3x4 = world_to_cam_mats[:, :3, :]
  
      return intrinsics, world_to_cam_3x4, world_points, img_points
  
file_path: kornia/test/geometry/calibration/test_pnp.py
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  from kornia.geometry.calibration.pnp import _mean_isotropic_scale_normalize
  from kornia.testing import assert_close, tensor_to_gradcheck_var
label_desc: Use image node_id 4 for calling the TestSolvePnpDlt obj's underlying member method code with example usage: obj._get_test_data(num_points, device, dtype) and returns: intrinsics,  world_to_cam_3x4,  world_points,  img_points